1. Field of the Invention
The current invention relates to the field of signal equalization, particularly to enabling improved high-speed adaptive equalization.
2. Description of Related Art
Increased demand for high-speed communications services has required that economical and efficient new devices and techniques be developed to support performance increases. For example, as transmission rates climb to the 10–40 Gbps range and beyond in modern optical networks, signal processing and conditioning techniques must be applied to filter out noise and reduce interference such as inter-symbol interference (ISI). Typical optical networks are plagued by noise and bandwidth limitations caused by polarization mode dispersion, modal dispersion, chromatic dispersion, limited component bandwidth, and/or other undesired phenomena. Such effects often cause problems such as group delay distortion, frequency-related attenuation, and/or others. Furthermore, the ISI can be time varying due to a variety of causes such as physical vibration, mechanical stresses and temperature fluctuations. Typically, optical receivers may use devices such as equalizers to improve the overall performance of such systems and minimize the error rate. However, the implementation of such devices has proven to be challenging and costly.
Equalizers based on transversal filters have been promoted as a way of removing noise and inter-symbol interference in some systems. For example, FIG. 1 (prior art) illustrates an example of a proposed transversal filter based equalizer 10 controlled by a microprocessor 50. In this example, the coefficients for the transversal filter may be set by the adaptation logic module, a microprocessor 50, based on analysis of eye monitor 30 data. For example, FIGS. 2a and b illustrate examples of eye patterns. Ideally, the eye monitor output would correlate well with bit-error rate (BER) of the sampled data. For example, the Mean Squared Error (MSE) of the eye measured at the sampling point should correlate with the bit-error rate for a signal with additive white Gaussian noise (AWGN). MSE-based eye monitors can be used to optimize eyes even in the presence of large amounts of ISI such as that shown in FIG. 2b. In practice, eye monitors will not be perfect predictors of BER. For example, the eye monitor's sampling point may be offset relative to the decision point. The decision threshold may also be different between the eye monitor and the data sampler. Actual noise characteristics might differ from those assumed in designing the eye monitor. For example, multiplicative noise may predominate in the actual system, even though additive noise may have been assumed in the design of the eye monitor.
In order to get a better prediction of BER, equalizers using forward-error-correction (FEC) feedback have been used. For example, Haunstein, Schlenk, and Sticht describe a method that compares the corrected data output of an FEC with the uncorrected data and uses the result to adjust coefficients of an equalizer. One of the benefits of using FEC feedback to determine coefficients of an equalizer is that the FEC feedback information is a good indication of the bit error rate, which can be effectively minimized. However, equalization based on FEC feedback typically requires a low bit error rate associated with FEC decoding. Higher bit error rates, which may be associated with noise and/or interference such as ISI, can cause the equalization process to converge too slowly or fail to converge at all. Thus, equalization based on FEC feedback may very well be inoperable under certain noise and/or interference conditions. FEC decoding may become impossible when the input BER is large. For example, an FEC processor may not decode the signal shown in FIG. 2b correctly, and thus the BER estimate would be meaningless.
Accordingly, it is desirable to achieve high-speed adaptive equalization that can effectively operate, even for systems experiencing severe distortion. This equalizer should be able to obtain comparable performance as one optimized using BER.